{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# RNN Tensorflow"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AAPL'\n",
        "start = '2012-01-01'\n",
        "end = '2018-01-01'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012-01-03</th>\n",
              "      <td>58.485714</td>\n",
              "      <td>58.928570</td>\n",
              "      <td>58.428570</td>\n",
              "      <td>58.747143</td>\n",
              "      <td>51.269413</td>\n",
              "      <td>75555200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-04</th>\n",
              "      <td>58.571430</td>\n",
              "      <td>59.240002</td>\n",
              "      <td>58.468571</td>\n",
              "      <td>59.062859</td>\n",
              "      <td>51.544937</td>\n",
              "      <td>65005500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-05</th>\n",
              "      <td>59.278572</td>\n",
              "      <td>59.792858</td>\n",
              "      <td>58.952858</td>\n",
              "      <td>59.718571</td>\n",
              "      <td>52.117188</td>\n",
              "      <td>67817400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-06</th>\n",
              "      <td>59.967144</td>\n",
              "      <td>60.392857</td>\n",
              "      <td>59.888573</td>\n",
              "      <td>60.342857</td>\n",
              "      <td>52.662014</td>\n",
              "      <td>79573200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-09</th>\n",
              "      <td>60.785713</td>\n",
              "      <td>61.107143</td>\n",
              "      <td>60.192856</td>\n",
              "      <td>60.247143</td>\n",
              "      <td>52.578468</td>\n",
              "      <td>98506100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Open       High        Low      Close  Adj Close    Volume\n",
              "Date                                                                       \n",
              "2012-01-03  58.485714  58.928570  58.428570  58.747143  51.269413  75555200\n",
              "2012-01-04  58.571430  59.240002  58.468571  59.062859  51.544937  65005500\n",
              "2012-01-05  59.278572  59.792858  58.952858  59.718571  52.117188  67817400\n",
              "2012-01-06  59.967144  60.392857  59.888573  60.342857  52.662014  79573200\n",
              "2012-01-09  60.785713  61.107143  60.192856  60.247143  52.578468  98506100"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,0)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,0)\n",
        "dataset['Returns'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Increase_Decrease</th>\n",
              "      <th>Buy_Sell_on_Open</th>\n",
              "      <th>Buy_Sell</th>\n",
              "      <th>Returns</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012-01-04</th>\n",
              "      <td>58.571430</td>\n",
              "      <td>59.240002</td>\n",
              "      <td>58.468571</td>\n",
              "      <td>59.062859</td>\n",
              "      <td>51.544937</td>\n",
              "      <td>65005500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.005374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-05</th>\n",
              "      <td>59.278572</td>\n",
              "      <td>59.792858</td>\n",
              "      <td>58.952858</td>\n",
              "      <td>59.718571</td>\n",
              "      <td>52.117188</td>\n",
              "      <td>67817400</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.011102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-06</th>\n",
              "      <td>59.967144</td>\n",
              "      <td>60.392857</td>\n",
              "      <td>59.888573</td>\n",
              "      <td>60.342857</td>\n",
              "      <td>52.662014</td>\n",
              "      <td>79573200</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.010454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-09</th>\n",
              "      <td>60.785713</td>\n",
              "      <td>61.107143</td>\n",
              "      <td>60.192856</td>\n",
              "      <td>60.247143</td>\n",
              "      <td>52.578468</td>\n",
              "      <td>98506100</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.001586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-10</th>\n",
              "      <td>60.844284</td>\n",
              "      <td>60.857143</td>\n",
              "      <td>60.214287</td>\n",
              "      <td>60.462856</td>\n",
              "      <td>52.766739</td>\n",
              "      <td>64549100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.003581</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Open       High        Low      Close  Adj Close    Volume  \\\n",
              "Date                                                                          \n",
              "2012-01-04  58.571430  59.240002  58.468571  59.062859  51.544937  65005500   \n",
              "2012-01-05  59.278572  59.792858  58.952858  59.718571  52.117188  67817400   \n",
              "2012-01-06  59.967144  60.392857  59.888573  60.342857  52.662014  79573200   \n",
              "2012-01-09  60.785713  61.107143  60.192856  60.247143  52.578468  98506100   \n",
              "2012-01-10  60.844284  60.857143  60.214287  60.462856  52.766739  64549100   \n",
              "\n",
              "            Increase_Decrease  Buy_Sell_on_Open  Buy_Sell   Returns  \n",
              "Date                                                                 \n",
              "2012-01-04                  1                 1         1  0.005374  \n",
              "2012-01-05                  1                 1         1  0.011102  \n",
              "2012-01-06                  1                 1         0  0.010454  \n",
              "2012-01-09                  0                 1         1 -0.001586  \n",
              "2012-01-10                  0                 0         0  0.003581  "
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": [
              "(1508, 10)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Data cleaning\n",
        "dataset.isna().any()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": [
              "Open                 False\n",
              "High                 False\n",
              "Low                  False\n",
              "Close                False\n",
              "Adj Close            False\n",
              "Volume               False\n",
              "Increase_Decrease    False\n",
              "Buy_Sell_on_Open     False\n",
              "Buy_Sell             False\n",
              "Returns              False\n",
              "dtype: bool"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 1508 entries, 2012-01-04 to 2017-12-29\n",
            "Data columns (total 10 columns):\n",
            "Open                 1508 non-null float64\n",
            "High                 1508 non-null float64\n",
            "Low                  1508 non-null float64\n",
            "Close                1508 non-null float64\n",
            "Adj Close            1508 non-null float64\n",
            "Volume               1508 non-null int32\n",
            "Increase_Decrease    1508 non-null int32\n",
            "Buy_Sell_on_Open     1508 non-null int32\n",
            "Buy_Sell             1508 non-null int32\n",
            "Returns              1508 non-null float64\n",
            "dtypes: float64(6), int32(4)\n",
            "memory usage: 106.0 KB\n"
          ]
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Open'].plot(figsize=(16, 6))\n",
        "dataset['Adj Close'].plot(figsize=(16, 6))\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Close: 30 Day Mean'] = dataset['Adj Close'].rolling(window=30).mean()\n",
        "dataset[['Adj Close', 'Close: 30 Day Mean']].plot(figsize=(16, 6))\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Dataset"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = dataset['Adj Close']\n",
        "train_data = pd.DataFrame(train_data)"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Scaling\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler(feature_range = (0, 1))\n",
        "train_data_scaled = sc.fit_transform(train_data)"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16, 6))\n",
        "plt.plot(train_data_scaled)\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a data structure with 60 timesteps and 1 output\n",
        "past_days = 60\n",
        "X_train = []\n",
        "y_train = []\n",
        "for i in range(past_days, len(train_data_scaled)):\n",
        "  X_train.append(train_data_scaled[i-past_days:i, 0])\n",
        "  y_train.append(train_data_scaled[i, 0])\n",
        "X_train, y_train = np.array(X_train), np.array(y_train)\n",
        "\n",
        "# Reshaping\n",
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
        "\n",
        "# Cast\n",
        "X_train = X_train.astype(np.float32)\n",
        "y_train = y_train.astype(np.float32)"
      ],
      "outputs": [],
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We predict the price of next day given past 60 days prices\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1448, 60, 1)\n",
            "(1448,)\n"
          ]
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Build a Model"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers"
      ],
      "outputs": [],
      "execution_count": 14,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential()"
      ],
      "outputs": [],
      "execution_count": 15,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding the first LSTM layer\n",
        "model.add(layers.LSTM(units=50, input_shape=(past_days, 1)))\n",
        "\n",
        "# Adding the output layer\n",
        "model.add(layers.Dense(units=1))"
      ],
      "outputs": [],
      "execution_count": 16,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 50)                10400     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 51        \n",
            "=================================================================\n",
            "Total params: 10,451\n",
            "Trainable params: 10,451\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "execution_count": 17,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compiling the RNN\n",
        "model.compile(optimizer=tf.train.AdamOptimizer(0.001),\n",
        "              loss='mean_squared_error')"
      ],
      "outputs": [],
      "execution_count": 19,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the RNN to the Training set\n",
        "model.fit(X_train, y_train, epochs=10, batch_size=32)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1448/1448 [==============================] - 2s 1ms/step - loss: 0.0305\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 2/10\n",
            "1448/1448 [==============================] - 1s 1ms/step - loss: 0.0011\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 3/10\n",
            "1448/1448 [==============================] - 2s 1ms/step - loss: 6.2046e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 4/10\n",
            "1448/1448 [==============================] - 2s 1ms/step - loss: 5.8294e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 5/10\n",
            "1448/1448 [==============================] - 2s 1ms/step - loss: 5.6334e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 6/10\n",
            "1448/1448 [==============================] - 2s 1ms/step - loss: 5.6394e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 7/10\n",
            "1448/1448 [==============================] - 1s 1ms/step - loss: 5.3401e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 8/10\n",
            "1448/1448 [==============================] - 1s 1ms/step - loss: 5.2094e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 9/10\n",
            "1448/1448 [==============================] - 2s 1ms/step - loss: 5.1044e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 10/10\n",
            "1448/1448 [==============================] - 2s 1ms/step - loss: 4.8445e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x1ce5bf36e10>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 20,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test Dataset"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = yf.download(symbol,start='2018-01-01', end='2019-01-01')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        }
      ],
      "execution_count": 21,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 22,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-02</th>\n",
              "      <td>170.160004</td>\n",
              "      <td>172.300003</td>\n",
              "      <td>169.259995</td>\n",
              "      <td>172.259995</td>\n",
              "      <td>167.701889</td>\n",
              "      <td>25555900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-03</th>\n",
              "      <td>172.529999</td>\n",
              "      <td>174.550003</td>\n",
              "      <td>171.960007</td>\n",
              "      <td>172.229996</td>\n",
              "      <td>167.672668</td>\n",
              "      <td>29517900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-04</th>\n",
              "      <td>172.539993</td>\n",
              "      <td>173.470001</td>\n",
              "      <td>172.080002</td>\n",
              "      <td>173.029999</td>\n",
              "      <td>168.451508</td>\n",
              "      <td>22434600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05</th>\n",
              "      <td>173.440002</td>\n",
              "      <td>175.369995</td>\n",
              "      <td>173.050003</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>170.369385</td>\n",
              "      <td>23660000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08</th>\n",
              "      <td>174.350006</td>\n",
              "      <td>175.610001</td>\n",
              "      <td>173.929993</td>\n",
              "      <td>174.350006</td>\n",
              "      <td>169.736588</td>\n",
              "      <td>20567800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2018-01-02  170.160004  172.300003  169.259995  172.259995  167.701889   \n",
              "2018-01-03  172.529999  174.550003  171.960007  172.229996  167.672668   \n",
              "2018-01-04  172.539993  173.470001  172.080002  173.029999  168.451508   \n",
              "2018-01-05  173.440002  175.369995  173.050003  175.000000  170.369385   \n",
              "2018-01-08  174.350006  175.610001  173.929993  174.350006  169.736588   \n",
              "\n",
              "              Volume  \n",
              "Date                  \n",
              "2018-01-02  25555900  \n",
              "2018-01-03  29517900  \n",
              "2018-01-04  22434600  \n",
              "2018-01-05  23660000  \n",
              "2018-01-08  20567800  "
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 22,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset.info()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 251 entries, 2018-01-02 to 2018-12-31\n",
            "Data columns (total 6 columns):\n",
            "Open         251 non-null float64\n",
            "High         251 non-null float64\n",
            "Low          251 non-null float64\n",
            "Close        251 non-null float64\n",
            "Adj Close    251 non-null float64\n",
            "Volume       251 non-null int32\n",
            "dtypes: float64(5), int32(1)\n",
            "memory usage: 12.7 KB\n"
          ]
        }
      ],
      "execution_count": 23,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = test_dataset['Adj Close']\n",
        "test_data = pd.DataFrame(test_data)"
      ],
      "outputs": [],
      "execution_count": 24,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.info()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 251 entries, 2018-01-02 to 2018-12-31\n",
            "Data columns (total 1 columns):\n",
            "Adj Close    251 non-null float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 3.9 KB\n"
          ]
        }
      ],
      "execution_count": 25,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Scaling\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler(feature_range = (0, 1))\n",
        "test_set_scaled = sc.fit_transform(test_data)"
      ],
      "outputs": [],
      "execution_count": 26,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_set_scaled = pd.DataFrame(test_set_scaled)\n",
        "test_set_scaled.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>0</th>\n",
              "      <td>0.271009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.270658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.279993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.302980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.295396</td>\n",
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            "text/plain": [
              "          0\n",
              "0  0.271009\n",
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      "execution_count": 27,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Getting the predicted stock price of 2018\n",
        "dataset_total = pd.concat((dataset['Adj Close'], test_dataset['Adj Close']), axis = 0)\n",
        "inputs = dataset_total[len(dataset_total) - len(test_dataset) - past_days:].values\n",
        "inputs = inputs.reshape(-1,1)\n",
        "inputs = sc.transform(inputs)\n",
        "X_test = []\n",
        "y_test = []\n",
        "\n",
        "prediction_days = test_dataset.shape[0]\n",
        "for i in range(past_days, past_days + prediction_days):\n",
        "  X_test.append(inputs[i-past_days:i, 0])\n",
        "  y_test.append(inputs[i, 0])\n",
        "  \n",
        "X_test, y_test = np.array(X_test), np.array(y_test)\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
        "\n",
        "# Cast\n",
        "X_test = X_test.astype(np.float32)\n",
        "y_test = y_test.astype(np.float32)"
      ],
      "outputs": [],
      "execution_count": 35,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_stock_price = model.predict(X_test)\n",
        "predicted_stock_price = sc.inverse_transform(predicted_stock_price)"
      ],
      "outputs": [],
      "execution_count": 36,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualising the results\n",
        "real_stock_price = test_dataset['Adj Close'].values\n",
        "plt.plot(real_stock_price, color='red', label='Real Stock Price')\n",
        "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
        "plt.title('Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 37,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result = model.evaluate(X_test, y_test)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "251/251 [==============================] - 0s 617us/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
          ]
        }
      ],
      "execution_count": 38,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Mean Squared Error: {0:.2%}\".format(result))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 0.55%\n"
          ]
        }
      ],
      "execution_count": 39,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
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